Deep Learning Models for Cybersecurity: A Comparative Analysis of CNN and RNN Architectures

Abstract

Cybersecurity has increasingly turned to deep learning models to address the growing complexity of cyber threats. This paper provides a comparative analysis of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in detecting various types of cyber attacks, including malware, phishing, and network intrusions. The research examines how CNNs, traditionally used in image processing, can be adapted for network traffic analysis, while RNNs are leveraged for sequence data in detecting attack patterns over time. The paper discusses the performance of these models in terms of accuracy, detection speed, and false-positive rates. It also explores the training requirements for each architecture and the datasets used to benchmark their performance. The potential for hybrid models that combine CNN and RNN elements to enhance cybersecurity is also discussed, along with the challenges posed by adversarial attacks on deep learning systems. The paper concludes with recommendations for deploying these models in real-world cybersecurity applications.